Systems, methods, and media for manufacturing processes

Abstract

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

The invention claimed is: 
     
       1. A manufacturing system, comprising:
 one or more stations, each station configured to perform at least one step in a multi-step manufacturing process for a specimen; 
 a monitoring platform configured to monitor progression of the specimen throughout the multi-step manufacturing process; and 
 a control module configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the specimen, the control module configured to perform operations, comprising:
 receiving, from the monitoring platform, an image of the specimen at a step of the multi-step manufacturing process; 
 extracting a feature vector from the image of the specimen; 
 generating, by the control module using a convolutional neural network, a final quality metric prediction based on the image of the specimen and the feature vector, wherein a final quality metric is a metric associated with a property of a completed specimen that cannot be measured until each step of the multi-step manufacturing process is complete; 
 determining, by the control module, that the final quality metric prediction is not within a range of acceptable values; and 
 based on the determining, adjusting by the control module, control logic for at least a following station, wherein the adjusting comprises applying a corrective action to be performed by the following station. 
 
 
     
     
       2. The manufacturing system of  claim 1 , wherein the operations further comprise:
 training the convolutional neural network to generate the final quality metric prediction based on a plurality of images of a plurality of specimens. 
 
     
     
       3. The manufacturing system of  claim 2 , wherein the operations further comprise:
 training a clustering module to label a plurality of images of a plurality of specimens for training of the convolutional neural network configured to generate the final quality metric prediction based on the plurality of images. 
 
     
     
       4. The manufacturing system of  claim 1 , wherein adjusting by the control module, the control logic for at least the following station, comprises:
 adjusting a further control logic for a further following station. 
 
     
     
       5. The manufacturing system of  claim 1 , wherein each of the one or more stations correspond to a layer deposition in a 3D printing process. 
     
     
       6. A multi-step manufacturing method, comprising:
 receiving, by a computing system from a monitoring platform of a manufacturing system, an image of a specimen at a station of one or more stations, each station configured to perform a step of a multi-step manufacturing process; 
 extracting a feature vector from the image of the specimen; 
 generating, by the computing system using a convolutional neural network, a final quality metric prediction based on the image of the specimen and the feature vector, wherein a final quality metric is a metric associated with a property of a completed specimen that cannot be measured until each step of the multi-step manufacturing process is complete; 
 determining, by the computing system, that the final quality metric prediction is not within a range of acceptable values; and 
 based on the determining, adjusting, by the computing system, control logic for at least a following station, wherein the adjusting comprising a corrective action to be performed by the following station. 
 
     
     
       7. The multi-step manufacturing method of  claim 6 , further comprising:
 training the convolutional neural network to generate the final quality metric prediction based on a plurality of images of a plurality of specimens. 
 
     
     
       8. The multi-step manufacturing method of  claim 7 , further comprising:
 training a clustering module to label a plurality of images of a plurality of specimens for training of the convolutional neural network configured to generate the final quality metric prediction based on the plurality of images. 
 
     
     
       9. The multi-step manufacturing method of  claim 6 , wherein adjusting, by the computing system, the control logic for at least the following station, comprises:
 adjusting a further control logic for a further following station. 
 
     
     
       10. The multi-step manufacturing method of  claim 6 , wherein each of the one or more stations correspond to a layer deposition in a 3D printing process. 
     
     
       11. A three-dimensional (3D) printing system, comprising:
 a processing station configured to deposit a plurality of layers to form a specimen; 
 a monitoring platform configured to monitor progression of the specimen throughout a deposition process; and 
 a control module configured to dynamically adjust processing parameters for each layer of the plurality of layers to achieve a desired final quality metric for the specimen, the control module configured to perform operations, comprising:
 receiving, from the monitoring platform, an image of the specimen after a layer has been deposited; 
 extracting a feature vector from the image of the specimen; 
 generating, by the control module using a convolutional neural network, a final quality metric prediction based on the image of the specimen and the feature vector, wherein a final quality metric is a metric associated with a property of a completed specimen that cannot be measured until each layer of the plurality of layers has been deposited; 
 determining, by the control module, that the final quality metric prediction is not within a range of acceptable values; and 
 based on the determining, adjusting, by the control module, control logic for at least a following layer to be deposited, wherein the adjusting comprising a corrective action to be performed by deposition of the following layer. 
 
 
     
     
       12. The 3D printing system of  claim 11 , wherein the operations further comprise:
 training the convolutional neural network to generate the final quality metric prediction based on a plurality of images of a plurality of specimens. 
 
     
     
       13. The 3D printing system of  claim 12 , wherein the operations further comprise:
 training a clustering module to label a plurality of images of a plurality of specimens for training of the convolutional neural network configured to generate the final quality metric prediction based on the plurality of images. 
 
     
     
       14. The 3D printing system of  claim 11 , wherein adjusting, by the control module, the control logic for at least the following layer, comprises:
 adjusting a further control logic for a further following layer.